The method of separation of variables facilitates the integration of the Hamilton-Jacobi equation by reducing its solution to a series of quadratures in the separable coordinates. The case in which the metric tensor is diagonal in the separable coordinates, that is, orthogonal separability, is fundamental. Recent theory by Benenti has established a concise geometric (coordinate-independent) characterisation of orthogonal separability of the Hamilton-Jacobi equation on a pseudoRiemannian manifold. It generalises an approach initiated by Eisenhart and developed by Kalnins and Miller. Benenti has shown that the orthogonal separability of a system via a point transformation is equivalent to the existence of a Killing tensor with real simple eigen values and orthogonally integrable eigenvectors. Applying a moving frame formalism, we develop a method that produces the orthogonal separable coordinates for low dimensional Hamiltonian systems. The method is applied to a two dimensional Riemannian manifold of arbitrary curvature. As an illustration, we investigate Euclidean 2-space, and the two dimensional surfaces of constant curvature, recovering known results. Using our formalism, we also derive the known superseparable potentials for Euclidean 2-space. Some of the original results presented in this thesis were announced in [8, 9, 10].

The method of separation of variables facilitates the integration of the Hamilton-Jacobi equation by reducing its solution to a series of quadratures in the separable coordinates. The case in which the metric tensor is diagonal in the separable coordinates, that is, orthogonal separability, is fundamental. Recent theory by Benenti has established a concise geometric (coordinate-independent) characterisation of orthogonal separability of the Hamilton-Jacobi equation on a pseudoRiemannian manifold. It generalises an approach initiated by Eisenhart and developed by Kalnins and Miller. Benenti has shown that the orthogonal separability of a system via a point transformation is equivalent to the existence of a Killing tensor with real simple eigen values and orthogonally integrable eigenvectors. Applying a moving frame formalism, we develop a method that produces the orthogonal separable coordinates for low dimensional Hamiltonian systems. The method is applied to a two dimensional Riemannian manifold of arbitrary curvature. As an illustration, we investigate Euclidean 2-space, and the two dimensional surfaces of constant curvature, recovering known results. Using our formalism, we also derive the known superseparable potentials for Euclidean 2-space. Some of the original results presented in this thesis were announced in [8, 9, 10].

Ο σκοπός της εργασίας είναι η παρουσίαση της έννοιας της συμμετρίας ως έναν μετασχηματισμό που απεικονίζει τη λύση μιας Δ.Ε. σε μια άλλη Δ.Ε. διατηρώντας αναλλοίωτη και αμετάβλητη τη μορφή της. Παρουσιάζεται επίσης η μέθοδος της αναλλοίωτης διαφόρισης και ο αλγόριθμος Lie. / -

Indoor positioning systems (IPS) has seen an increase in demand because of the needto locate users in environments where Global Navigation Satellite Systems (GNSS) lacksaccuracy. The current way of implementing an IPS is often tedious and time consuming.However, with the improvements of position estimation and object detection on phones,a lightweight and low-cost solution could become the standard for the implementationphase of an IPS. Apple recently included a Light Detection And Ranging (LiDAR) sensorin their phones, greatly improving the phones depth measurements and depth understanding.This allows for a more accurate virtual representation of an environment. This thesisstudies the accuracy of ARKit’s reconstructed world and how different environments impactthe accuracy. The thesis also investigates the use of reference points as a tool to map thereconstructed environment to a geo-referenced map, such as Google Maps and Open StreetMap. The results show that ARKit can create virtual representations with centimetre levelaccuracy for small to medium sized environments. For larger or vertical environments,such as corridors or staircases, ARKit’s SLAM algorithm no longer recognizes previouslyvisited areas, causing both duplicated virtual environments and large drift errors. With theuse of multiple reference points, we showed that ARKit can and should be considered asa viable tool for scanning and mapping small scale environments to geo-referenced floorplans.